CN115830807A - Mine safety early warning method, system, equipment and medium - Google Patents
Mine safety early warning method, system, equipment and medium Download PDFInfo
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Abstract
The application relates to a mine safety early warning method, a system, equipment and a medium, and the method specifically comprises the following steps: acquiring aerial global images of a mine and surrounding regions thereof by an unmanned aerial vehicle; establishing a three-dimensional model of the region where the mine is located and the region around the mine according to the aerial global image; acquiring rainfall information of a region where a mine is located and a region around the mine; leading precipitation information of the three-dimensional model, the region where the mine is located and the region around the mine into a preset neural network prediction model, and predicting the disaster probability of the mine; and sending out safety early warning according to the disaster probability. According to the method and the device, the data of main influence factors possibly causing mine disasters are obtained by establishing the three-dimensional models of the mine and the surrounding areas of the mine, the related data are input into the neural network prediction model to predict the water accumulation grade of the mine, the early prediction of the flood disasters of the mine is realized, and early warning is timely sent to personnel to prompt the personnel to take corresponding measures before the disasters occur.
Description
Technical Field
The application relates to the field of safety early warning, in particular to a mine safety early warning method, a mine safety early warning system, mine safety early warning equipment and a mine safety early warning medium.
Background
With the scientific and technological progress of society, digitalization and intellectualization are slowly integrated with the aspects of life and production, and for the current mineral industry, the traditional mine gradually changes to a digital mine. The mine is usually selected in a mountain area rich in mineral resources and is a special geographical position of the mountain area, so that influence of natural disasters on the mine is huge, and once the mine has natural disasters such as water accumulation or flood, huge disasters and losses are caused to industrial and agricultural production and life and property of people.
At present, the safety early warning of the mine usually adopts manual work to periodically investigate aspects such as water accumulation amount and a drainage system of the mine, or various sensors are used for acquiring various data of the mine to analyze the dangerous cases possibly occurring in the mine.
By the mode, only the mine is considered, the influence of rainfall and other factors on the mine in the upstream and downstream areas or the peripheral areas of the mine is not considered, and safety prompt cannot be accurately given to personnel.
Disclosure of Invention
In order to accurately give a safety prompt to personnel, the application provides a mine safety early warning method, a system, equipment and a medium.
The first aspect of the present application provides a mine safety early warning method, which adopts the following technical scheme:
acquiring aerial global images of the region where the mine is located and the region around the mine by an unmanned aerial vehicle;
establishing a three-dimensional model of the region where the mine is located and the region around the mine according to the aerial global image;
acquiring rainfall information of the region where the mine is located and the region around the mine;
leading precipitation information of the three-dimensional model, the region where the mine is located and the region around the mine into a preset neural network prediction model, and outputting a prediction result, wherein the prediction result is the water accumulation grade of the mine;
and sending out safety early warning according to the prediction result.
By adopting the technical scheme, the basic data of mine disaster prediction is acquired through global photography of the unmanned aerial vehicle, the mine disaster prediction model is generated through learning and feedback of the neural network model, the acquired disaster prediction basic data is imported into the prediction model, the mine disaster prediction is completed, the safety early warning information is generated in time according to the prediction result output by the prediction model, relevant personnel are prompted to quickly make corresponding reactions, and flood disasters which may occur in the mine are prevented and treated.
Preferably, unmanned aerial vehicle's camera device include four distribute in unmanned aerial vehicle all around and with the slope camera, one that ground is certain inclination set up in the perpendicular camera on unmanned aerial vehicle bottom and perpendicular to ground.
Through adopting above-mentioned technical scheme, make unmanned aerial vehicle camera device can all-round acquire the global image data in mine and peripheral region, compare in the setting of single camera, the regular setting of many cameras can be more accurate acquire information such as relief information, the mine overall structure of mine in mine and peripheral region, provide accurate data for subsequent three-dimensional modeling.
Preferably, the step of obtaining the aerial global image of the region where the mine is located and the region around the mine through the unmanned aerial vehicle specifically comprises the following steps:
determining the cruise period of the unmanned aerial vehicle according to a preset unmanned aerial vehicle cruise meter;
and shooting aerial global images of the region where the mine is located and the region around the mine according to the unmanned aerial vehicle cruising period.
By adopting the technical scheme, the terrain information of the mine and the surrounding area thereof is regularly shot, so that the influence of climate change or human factors on the terrain information of the mine and the surrounding area thereof is prevented, the obtained global image is ensured to have little difference with the actual condition, and the accuracy of the subsequent prediction result is ensured.
Preferably, the three-dimensional model of the region where the mine is located and the region around the mine and the neural network prediction model are iterated according to real-time aerial global images shot by the unmanned aerial vehicle in regular cruise.
By adopting the technical scheme, the three-dimensional model of the region where the mine is located and the region around the mine and the neural network prediction model are iterated according to the change of the actual condition, so that the prediction result output by the prediction model is relatively accurate, and the fitting degree of the prediction result and the actual condition is higher.
Preferably, the step of importing the three-dimensional model and precipitation information of the area where the mine is located and the area around the mine into a neural network prediction model to predict the disaster probability of the mine specifically includes the following steps:
extracting the terrain characteristics of the mine and the surrounding regions of the mine according to the three-dimensional model;
calculating the maximum water storage capacity and the drainage capacity of the mine according to the three-dimensional model;
and inputting the maximum water storage capacity of the mine, the drainage capacity of the mine, the terrain characteristics of the mine and the region around the mine and the precipitation information as input data into a neural network prediction model, and outputting a prediction result.
By adopting the technical scheme, the maximum water storage capacity of the mine, the mine drainage capacity, the terrain characteristics of the mine and the surrounding areas of the mine and rainfall information are introduced as main influence factors which can influence the occurrence possibility of the flood disaster of the mine, the input value directly represents the relation between the whole water storage capacity of the mine and the drainage capacity of the mine, and the water storage condition of the mine in a certain time after rainfall in the surrounding areas of the mine is predicted by analyzing the water storage capacity and the rainfall of the mine; factors in all aspects causing mine disasters are considered from multiple angles, and accuracy of the prediction model is guaranteed.
Preferably, the neural network prediction model is generated by error correction feedback learning by taking the historical mine maximum water storage capacity, the mine drainage capacity, the mine and the terrain features of the region surrounding the mine and the precipitation information data as training sets and taking the historical mine ponding grade as a target result.
By adopting the technical scheme, in the learning process of the neural network prediction model, errors are fed back, and the weight of each input sample is timely adjusted according to the fed-back error information, so that the neural network prediction model can more accurately reflect the relation between an input value and an output value, and the obtained prediction result is more fit with a real value.
Preferably, the step of sending out the safety early warning according to the disaster probability specifically comprises the following steps:
obtaining a safety early warning level corresponding to the prediction result according to a preset safety level table;
and sending corresponding early warning information according to the safety early warning level to prompt personnel to react.
By adopting the technical scheme, different safety early warning information is sent out according to different prediction results, so that personnel can plan a pertinence strategy according to different safety early warning information, prompt early warning is effectively carried out on the personnel aiming at flood disasters which may happen, and the personnel can timely react to dangerous situations.
In a second aspect, the application provides a mine safety precaution system includes the following modules:
the aerial global image acquisition module is used for acquiring aerial global images of the region where the mine is located and the region around the mine;
the three-dimensional model generation module is used for establishing a three-dimensional model of the region where the mine is located and the region around the mine according to the aerial global image;
the rainfall information acquisition module is used for acquiring rainfall information of the region where the mine is located and the region around the mine;
the neural network prediction module is used for predicting the water accumulation grade of the mine through a neural network prediction model;
and the safety early warning module is used for sending out safety early warning according to the prediction result.
In a third aspect, the present application provides a computer device, which adopts the following technical solution: the mine safety early warning system comprises a memory and a processor, wherein the memory is stored with a computer program which can be loaded by the processor and can execute any one of the mine safety early warning methods.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions: and the mine safety early warning program can be loaded and executed by the processor.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the prediction of the mine disaster occurrence probability is realized through a neural network prediction model, the mine flood disaster caused by the precipitation in the surrounding area is predicted in time, and personnel are reminded to deal with the disaster in time;
2. different safety early warnings are sent to the personnel according to different prediction results, the personnel can be prompted to take corresponding measures according to different disaster probabilities, and the personnel can pertinently solve possible dangerous situations;
3. the mine and the surrounding aerial global images thereof are acquired by the unmanned aerial vehicle, a three-dimensional model of the mine and the surrounding aerial global images thereof are established, and data such as topographic features of the mine and surrounding areas thereof, the maximum water storage capacity of the mine and the like are analyzed by the three-dimensional model, so that personnel can know the geographical situation of the mine more visually.
Drawings
Fig. 1 is a flowchart of a method of a mine safety warning method according to an embodiment of the present application.
Fig. 2 is a system block diagram of a mine safety warning method according to an embodiment of the present application.
Fig. 3 is a flowchart of a climate unmanned aerial vehicle cruise strategy of the mine safety early warning method according to the embodiment of the application.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Description of reference numerals: 201. an aerial global image acquisition module; 202. a three-dimensional model generation module; 203. a precipitation information acquisition module; 204. a neural network prediction module; 205. a safety early warning module; 206. an unmanned aerial vehicle; 400. an electronic device; 401. a processor; 402. a communication bus; 403. a user interface; 404. a network interface; 405. a memory.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments.
The embodiment of the application discloses a mine safety early warning method, a mine safety early warning system, mine safety early warning equipment and a mine safety early warning medium.
Referring to fig. 1, a mine safety early warning method includes the following steps:
s101: acquiring aerial global images of the region where the mine is located and the region around the mine by using the unmanned aerial vehicle 206;
specifically, the mine and the surrounding area thereof may be affected by climate or human factors, for example, personnel construction and the like, so that the topographic features of the mine and the surrounding area thereof change, the unmanned aerial vehicle 206 may acquire the aerial global image of the mine and the surrounding area thereof at regular intervals, the unmanned aerial vehicle 206 cruises the mine and the surrounding area thereof at fixed intervals according to a preset unmanned aerial vehicle 206 cruise table, for example, the unmanned aerial vehicle 206 may be set to cruise at regular intervals every half year, and the unmanned aerial vehicle 206 photographs the aerial global image of the mine and the surrounding area thereof according to a pre-planned photographing path. The set unmanned aerial vehicle 206 cruise mode realizes regularly mastering the real-time conditions of the mine and the surrounding area thereof, and provides an accurate and practical data basis for the subsequent steps.
The mine and the surrounding area of the mine are shot through a camera shooting mechanism arranged on the unmanned aerial vehicle 206, the camera shooting mechanism of the unmanned aerial vehicle 206 is provided with five cameras, one camera is installed at the bottom of the unmanned aerial vehicle 206, the camera shooting angle is vertical to the ground, the rest four cameras are distributed around the unmanned aerial vehicle 206, and the camera shooting angle of the camera shooting mechanism forms a certain inclination angle with the ground; the unmanned aerial vehicle 206 shoots specific images of the mine and the surrounding area thereof in an oblique photography mode, including but not limited to images of the dam body of the mine, the topographic features of the surrounding area of the mine, the detailed textures of the mine and the surrounding area thereof, and the like; the mine periphery area may be set to all areas within 10 km of the mine periphery. Through a plurality of cameras that set up on unmanned aerial vehicle 206, the true condition in mine and peripheral region can be more clear reflection to can directly carry out direct measurement through the camera image that unmanned aerial vehicle 206 obtained to the element that mine and peripheral region contained.
The aerial global image shot by the drone 206 may be stored on a memory card inside the drone 206 first, and when the drone 206 returns, personnel receive the shot control global image by reading data of the memory card; a wireless communication module may also be installed on the drone 206, and the aerial global image taken by the drone 206 is directly wirelessly transmitted to the aerial global image acquisition module 201.
S102: establishing a three-dimensional model of the region where the mine is located and the region around the mine according to the aerial global image;
specifically, after the images of the mine and the surrounding area thereof are obtained, the obtained data are required to be used for carrying out three-dimensional modeling on the mine and the surrounding area thereof; and importing the acquired image information into three-dimensional modeling software, and establishing a three-dimensional model of the mine and the peripheral area thereof in the three-dimensional modeling software, wherein the three-dimensional modeling software can be PhotoSacan.
The three-dimensional model is regularly updated according to images of the current mine and the surrounding area of the current mine, which are shot by the unmanned aerial vehicle 206 in a cruising manner, so that the three-dimensional model is closer to the actual situation, and the accuracy of follow-up prediction is ensured.
S103: acquiring rainfall information of the region where the mine is located and the region around the mine;
specifically, precipitation information of the surrounding area is obtained, the precipitation information includes, but is not limited to, precipitation amount, precipitation time and other data, all precipitation information of the surrounding area of the mine can be comprehensively obtained, and after a plurality of areas with the largest influence of surrounding precipitation on the mine are obtained through rough analysis according to the three-dimensional model, precipitation information of the plurality of areas is only obtained.
Precipitation information can be acquired by a climate unmanned aerial vehicle, the climate unmanned aerial vehicle regularly cruises specific nodes set by personnel in the peripheral area of the mine, and the specific nodes are defined according to three-dimensional modeling of the peripheral area of the mine; referring to fig. 3, the cruise strategy of the climate unmanned aerial vehicle for the characteristic node is as follows, when the area where the mine is located is rainfall, the climate unmanned aerial vehicle immediately starts cruising for the specific node, and the cruise interval time is 1h each time; if the area where the mine is located does not generate precipitation, the climate unmanned aerial vehicle cruises according to a starting command, wherein the starting command can be generated according to three bases, and one of the three bases is that when personnel sense that the area where the mine is located has abnormal climate or can generate flooding, the climate unmanned aerial vehicle cruises by sending a signal through a controller such as a mobile phone; secondly, weather forecast information is received, precipitation in the periphery area of the defined mine is determined by recognizing key words in the weather forecast information, a starting command is generated, the climate unmanned aerial vehicle is started to cruise, and the designated nodes are periodically monitored according to a preset path, wherein the monitoring period can be 2 hours; and thirdly, when a temperature sensor arranged in the mine monitors that the mine has large temperature change, a starting command is generated, and the climate unmanned aerial vehicle cruises. Since the precipitation in the area surrounding the mine affects the area where the mine is located, when the temperature in the area where the mine is located changes, it is indicated that the area surrounding the mine may be or will be subjected to precipitation, and the temperature change mentioned above may change differently according to the season and time of the area where the mine is located, so that the temperature change can be set by a person skilled in the art according to actual conditions.
After the climate unmanned aerial vehicle reaches the designated node, the designated node can be arranged by personnel, the seepage influence of the designated node is small and negligible, the ground water accumulation information is shot and is image information, the ground water accumulation information is sent to a computer for analysis through wireless transmission, and the analysis model can be a pre-trained neural network model through a preset analysis model, so that the precipitation is obtained.
S104: leading the three-dimensional model and precipitation information of the region where the mine is located and the region around the mine into a preset neural network prediction model to predict the water accumulation grade of the mine;
specifically, before the probability of mine disaster is predicted by the neural network prediction model, various data of the mine need to be processed. According to the generated three-dimensional model, calculating to obtain the maximum water storage capacity of the mine, analyzing the drainage capacity of the mine, and analyzing the topographic features of the peripheral region of the mine, wherein the two items of data of the drainage capacity of the mine and the topographic features of the peripheral region of the mine are evaluated by personnel according to an index system, the maximum water storage capacity of the mine can be evaluated by a data analysis plug-in the three-dimensional model to construct an irregular triangular net calculation model, so that the calculation model is close to the topographic condition of the mine, and then the volume of the triangular net is calculated to obtain the maximum water storage capacity of the mine.
The preset neural network prediction model adopts a BP neural network, five items of data which mainly affect the occurrence probability of mine disasters, namely historical precipitation, precipitation time, mine topography information, mine maximum water storage capacity and mine drainage capacity, are used as a training set, the historical mine water accumulation grade is used as a target output value, the neural network prediction model is trained until the mean square error between a generated result and an ideal value is less than a certain value, and the initialization of the preset neural network prediction model is completed.
Through the process, the preset neural network prediction model completes initialization in the error correction process, and at the moment, current rainfall information, mine terrain information, the maximum mine water storage capacity and mine drainage capacity data are input into the neural network prediction model, so that the water accumulation grade of the mine in a certain time after the precipitation occurs in the area where the mine is located and the area around the mine is obtained.
The accumulated water grade is the evaluation of the integral accumulated water volume of the mine, the maximum accumulated water volume of the mine is comprehensively considered, and the accumulated water grade can be divided into first-level accumulated water, second-level accumulated water, third-level accumulated water and fourth-level accumulated water; the ponding grade reflects the ponding condition of the mine under the influence of precipitation, and the higher the ponding grade is, the larger the influence of precipitation of the area where the mine is located or the area around the mine is, the higher the probability of flood disasters of the mine is.
S105: sending out safety early warning according to the prediction result;
specifically, after the water accumulation grade of the mine is obtained, the maximum water storage capacity and the drainage capacity of the mine are comprehensively considered, and the flooding phenomenon which possibly occurs in the mine after precipitation occurs in the mine or the surrounding area of the mine is evaluated; according to the predicted mine water accumulation grade, a safety early warning is sent out by contrasting a safety early warning rule set in advance, for example, when the predicted mine water accumulation grade is first-level water accumulation, the precipitation is determined to have no influence on the drainage capacity of the mine, the probability of disaster occurrence is low, and the safety early warning is not sent to personnel under the condition; when the accumulated water grade of the mine is predicted to be secondary accumulated water, a primary safety early warning is sent to an intelligent end of a mine manager to prompt the manager to continuously pay attention to the accumulated water grade of the follow-up mine and pay attention to the occurrence of dangerous cases; when the grade of the accumulated water of the mine is predicted to be three-level accumulated water, a medium safety early warning is sent to intelligent terminals of the mine and upstream mine staff, the current water storage condition of the mine is described, and the staff is prompted to make flood prevention preparation as soon as possible; and when the water accumulation grade of the mine is predicted to be four-level water accumulation, the highest safety early warning is sent out, an obvious sound alarm is sent out, personnel are prompted to evacuate in time, and the current state information of the mine is sent to a related disaster processing unit.
The implementation principle of the mine safety early warning method in the embodiment of the application is as follows: the method comprises the steps of acquiring aerial global images of a mine and a peripheral area thereof through cruising of an unmanned aerial vehicle 206, importing acquired image data into three-dimensional modeling software to generate a three-dimensional model of the mine and the peripheral area thereof, acquiring precipitation information of the mine and the peripheral area thereof, extracting a characteristic value which possibly causes disasters of the mine according to the three-dimensional model and the precipitation information, inputting the characteristic value as an input quantity into a preset neural network prediction model, predicting the water accumulation grade of the mine, and generating different early warning information according to a prediction result to prompt personnel to take targeted measures on the dangerous case which possibly occurs.
The embodiment of the application also discloses a mine safety early warning system. Referring to fig. 2, a mine safety precaution system includes the following modules:
an aerial global image acquisition module 201, configured to acquire aerial global images of a region where a mine is located and a region around the mine;
the three-dimensional model generation module 202 is used for establishing a three-dimensional model of a region where the mine is located and a region around the mine according to the aerial global image;
the rainfall information acquisition module 203 is used for acquiring rainfall information of the region where the mine is located and the region around the mine;
the neural network prediction module 204 is used for predicting the water accumulation grade of the mine through a neural network prediction model;
and the safety early warning module 205 is used for sending out safety early warning according to the prediction result.
Referring to fig. 4, a schematic structural diagram of an electronic device 400 is provided in the present embodiment. As shown in fig. 4, the electronic device 400 may include: at least one processor 401, at least one network interface 404, a user interface 403, memory 405, at least one communication bus 402.
Wherein a communication bus 402 is used to enable connective communication between these components.
The user interface 403 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 403 may also include a standard wired interface and a wireless interface.
The network interface 404 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 405 may include a Random Access Memory (RAM) 405 or a Read-Only Memory (Read-Only Memory) 405. Optionally, the memory 405 includes a non-transitory computer-readable medium. The memory 405 may be used to store instructions, programs, code sets, or instruction sets. The memory 405 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store the data and the like referred to above in the respective method embodiments. The memory 405 may alternatively be at least one storage device located remotely from the aforementioned processor 401. As shown in fig. 4, the memory 405, which is a computer storage medium, may include therein an operating system, a network communication module, a user interface 403 module, and an application program of a mine safety warning method.
In the electronic device 400 shown in fig. 4, the user interface 403 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 401 may be used to invoke an application program of a mine safety pre-warning method stored in the memory 405, which when executed by the one or more processors 401, causes the electronic device 400 to perform the method as described in one or more of the above embodiments.
An electronic device 400 readable storage medium, the electronic device 400 readable storage medium having instructions stored thereon. When executed by the one or more processors 401, cause the electronic device 400 to perform the methods as described in one or more of the above embodiments.
The embodiment of the application also discloses a computer readable storage medium.
Specifically, the computer-readable storage medium stores a computer program capable of being loaded by the processor 401 and executing the mine safety precaution method, the computer-readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory 405 (ROM), a Random Access Memory 405 (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: all equivalent changes made according to the structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (10)
1. A mine safety early warning method is characterized by comprising the following steps:
acquiring aerial global images of the region where the mine is located and the region around the mine by an unmanned aerial vehicle (206);
establishing a three-dimensional model of the region where the mine is located and the region around the mine according to the aerial global image;
acquiring rainfall information of a region where a mine is located and a region around the mine;
leading precipitation information of the three-dimensional model, the region where the mine is located and the region around the mine into a preset neural network prediction model, and outputting a prediction result, wherein the prediction result is the water accumulation grade of the mine;
and sending out safety early warning according to the prediction result.
2. The mine safety early warning method according to claim 1, characterized in that:
unmanned aerial vehicle (206)'s camera device include four distribute in unmanned aerial vehicle (206) all around and with ground be the slope camera at certain inclination, one set up in unmanned aerial vehicle (206) bottom and perpendicular to ground's perpendicular camera.
3. The mine safety early warning method according to claim 1, wherein the step of obtaining the aerial global image of the region where the mine is located and the region around the mine through the unmanned aerial vehicle (206) specifically comprises the following steps:
determining the cruise cycle of the unmanned aerial vehicle (206) according to a preset cruise table of the unmanned aerial vehicle (206);
and shooting aerial global images of the region where the mine is located and the region around the mine according to the cruising period of the unmanned aerial vehicle (206).
4. The mine safety early warning method according to claim 1, characterized in that:
the three-dimensional model of the region where the mine is located and the region around the mine and the neural network prediction model are iterated according to real-time aerial global images shot by the unmanned aerial vehicle (206) in regular cruise.
5. The mine safety early warning method according to claim 1, wherein in the step of importing the three-dimensional model and precipitation information of the region where the mine is located and the region around the mine into a preset neural network prediction model and outputting a prediction result, the method specifically comprises the following steps:
extracting the terrain features of the mine and the region around the mine according to the three-dimensional model;
calculating the maximum water storage capacity and the drainage capacity of the mine according to the three-dimensional model;
and inputting the maximum water storage capacity of the mine, the drainage capacity of the mine, the terrain characteristics of the mine and the region around the mine and the precipitation information as input data into a neural network prediction model, and outputting a prediction result.
6. The mine safety early warning method according to claim 5, characterized in that:
the neural network prediction model is generated by taking the historical mine maximum water storage capacity, the mine drainage capacity, the mine and the terrain characteristics of the region around the mine and the precipitation information data as a training set and taking the historical mine ponding grade as a target result through error correction feedback learning.
7. The mine safety early warning method according to claim 1, wherein the step of sending out safety early warning according to disaster probability specifically comprises the following steps:
obtaining a safety early warning grade corresponding to the prediction result according to a preset safety grade table;
and sending corresponding early warning information according to the safety early warning level to prompt personnel to react.
8. A mine safety precaution system based on any one of claims 1-7, the system comprising the following modules:
the aerial global image acquisition module (201) is used for acquiring aerial global images of the region where the mine is located and the region around the mine;
the three-dimensional model generation module (202) is used for establishing a three-dimensional model of the region where the mine is located and the region around the mine according to the aerial global image;
the rainfall information acquisition module (203) is used for acquiring rainfall information of the region where the mine is located and the region around the mine;
the neural network prediction module (204) is used for predicting the water accumulation grade of the mine through the neural network prediction model;
and the safety early warning module (205) is used for sending out safety early warning according to the prediction result.
9. A computer device, characterized in that it comprises a memory (405) and a processor (401), said memory (405) having stored thereon a computer program that can be loaded by the processor (401) and that executes the method according to any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored which can be loaded by a processor (401) and which performs the method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117422991A (en) * | 2023-10-07 | 2024-01-19 | 中色地科蓝天矿产（北京）有限公司 | Intelligent mine detection system and method based on big data and readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117422991A (en) * | 2023-10-07 | 2024-01-19 | 中色地科蓝天矿产（北京）有限公司 | Intelligent mine detection system and method based on big data and readable storage medium |
| CN117422991B (en) * | 2023-10-07 | 2024-06-04 | 中色地科蓝天矿产（北京）有限公司 | Intelligent mine detection system and method based on big data and readable storage medium |
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